Modeling and Multi-Objective Optimization for Resilient, Sustainable, and Circular Supply Chains

Supply chains increasingly face the need to reconcile economic performance with resilience, environmental and social sustainability, and the transition toward circular resource flows. These objectives are inherently conflicting and require decision-support approaches capable of making trade-offs explicit rather than treating resilience or sustainability as isolated design attributes. This Research Topic aims to advance multi-objective optimization and quantitative modeling methods for strategic, tactical, and operational supply chain decisions. Particular emphasis will be placed on the formulation, solution, and interpretation of models integrating resilience, circularity, resource efficiency, emissions, service, risk, and economic performance under uncertainty. The collection seeks both methodological advances and rigorous applications demonstrating how Pareto-based optimization, mathematical programming, robust and stochastic optimization, simulation-optimization, decomposition, metaheuristics, and data-driven approaches can support transparent and implementable decisions in complex supply chain systems.Research on resilient and sustainable supply chains has expanded rapidly in response to geopolitical disruptions, climate-related events, demand volatility, resource scarcity, and technological change. At the same time, circular economy principles are introducing reverse flows, recovery options, remanufacturing, recycling, and resource-loop closure into supply chain design and planning. These developments create decision problems in which multiple performance dimensions must be considered simultaneously and under uncertainty. While conceptual frameworks, digital technologies, and resilience strategies are increasingly available, further advances are needed in formal optimization models that quantify competing objectives, characterize trade-offs, and translate them into actionable decisions across interconnected supply chain networks.We welcome Original Research, Methods, Review, Systematic Review, and Perspective articles whose central contribution is quantitative modeling, multi-objective optimization, or optimization-based decision support for supply chains. Relevant themes include:-Pareto optimization and trade-off analysis;-Multi-objective mathematical programming;-Robust, stochastic, fuzzy, and risk-aware optimization;-Circular and closed-loop network design;-Reverse logistics, recovery, remanufacturing, and resource allocation;-Resilience investment and disruption-response optimizationIntegrated economic-environmental-social objectives;-Simulation-optimization;-Decomposition and exact or hybrid solution methods;-Evolutionary and other multi-objective metaheuristics;-AI- or data-assisted optimization.Applications may include agri-food, bioenergy, manufacturing, healthcare, energy, and other sectors. Purely descriptive resilience or sustainability studies without a substantive quantitative decision-support contribution fall outside the intended focus. Topic editors: Rafael Guillermo García-Cáceres, Prof. Jose Arturo Garza-Reyes, Diana Ramirez-Rios Source: https://www.frontiersin.org/research-topics/85407/modeling-and-multi-objective-optimization-for-resilient-sustainable-and-circular-supply-chains